[pfduke02]

I’m in extreme agreement for part -- for a company to get value out of their data, you want someone skilled at data cleaning, cutting it properly, and teasing out the insights. Where I disagree is that the person can be a data scientist, but doesn’t need to be. I believe that there is a growing population of data savvy employees without that title, many of them might not even have data at all in their title (they are in business operations, marketing, finance, and sales) -- many of them write SQL and are very comfortable manipulating data in BI tools, R, Python, Excel, or GSheets.

I also believe that company context matters a lot. I think so much of getting started with extracting value from data is getting up the learning curve of understanding what it means (which columns have the truth). One of the reasons that we don’t have a lot of canned reports is that understanding these edge cases within a company often matters a lot (and that not accounting for the nuance can often lead to a misinference). With this in mind, the explosion of ETL solutions and products like Mozart Data means that others at the company can specialize in their business context, as opposed to needing someone who can do all aspects of data including engineering, data science, analysis, and communicating/presenting it.